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Multiplicity and Divergence Challenge the Social Sciences
Fred Phillips
Kondratieff Laureate Lecture presented at the 10th International Kondratieff Conference
“Academic Heritage of N. D. Kondratieff and the Present,” dedicated to the 125th Kondratieff
anniversary.
Members of the Russian Academy of Sciences, Directors of the International N.D.
Kondratieff Foundation, and members of the jury, thank you for this wonderful honor.
There are many more to whom I want to express my deepest gratitude – following a few
remarks about current challenges to the social sciences.
We are living in a world of multiples: Multiple products, multiple industries, multiple
careers, multiple cultures, multiple stakeholders, and multiple connections (Phillips 2008).
Fukuyama’s (1993) prediction of the end of history was too simplistic; Human societies
are both converging and diverging. Ashby’s Law (Ashby 1956) implies that these
multiplicities – which constantly re-arrange themselves – cannot be researched through a
single analytic lense. On the contrary, we need multiple perspectives, as advocated by our
mentor Harold Linstone (2010).
Unifying the social sciences?
This in turn implies that calls to unify the social sciences (e.g., that of Gerring 2005) are
misplaced. I hope such unification will not happen, because we need the multiple
perspectives contributed by the various social sciences – even though these perspectives
are sometimes contradictory, and though we do expect some convergence of perspectives
as scientists from the various disciplines succumb to the lure of ‘big data.’
A passage from Linstone and Mitroff (1993) brilliantly illustrates:
Consider… the problem of drug use and addiction…. [To an
educator] the problem is one of educating young people and their
families to the dangers of drug use…. In the language of economics, the
problem is the huge profits associated with the production and
consumption of illegal substances…. In the language of social work, the
problem is the breakdown of the family, the lack of male role models, and
so on. In medical terms, the problem is one of treating the physiology of
drug addiction. For the criminal justice system, the problem is…
money for policing. For psychology, [it] is the despair of people in inner
cities and the associated problems of low self-esteem…. Each [discipline]
uses different variables to structure the ‘problem,’ and consequently
collects very different kinds of data.
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Action taken to advance one group’s success metrics will exacerbate
the problems as they are seen by the other groups. The social worker’s
free meal center for street addicts will, from the perspective of the city
planner, make an already undesirable neighborhood even less attractive
to business investment, and, to the economist, create a disincentive to
gainful employment.
Duncan Watts (2017), a sociologist at Microsoft Research, recently dubbed this the
‘incoherency problem’ of social science. I see it instead as an opportunity, an opportunity
to hold a multi-disciplinary dialog – before attacking a problem – on what would constitute a
solution. This has never been tried. We cannot expect total agreement. Nonetheless, our
challenge as social scientists is to reconcile, or at least benefit from, these multiple views
post hoc, rather than restrict them a priori. Technological Forecasting & Social Change board
member Nebojša Nakićenović (2007) at IIASA has made progress on the post hoc
reconciliation task.
Interconnections, complexity and nonlinearity
We have long known that qualitative research techniques are right for structuring new
areas of inquiry – for discerning categories, assigning nomenclature, beginning to
hypothesize relationships, and formulating questions that the researcher hopes will later
be answered by quantitative and confirmatory methods. Quantitative methods prevail in
more well-established areas of inquiry.
We are now realizing that mature areas of inquiry do not wither and die, as traditional
life cycle theory might predict, but rather they connect with other disciplinary areas and
via these connections become more complex. One example in industry is the demise of
‘stand-alone’ software packages. It is unprecedented but not surprising to see software
companies joining INCOSE, the professional association of systems engineers (Roth
2017), in order to understand how to connect their product to multiple platforms, to
multiple intermediaries like Akamai (the Internet most definitely did not ‘eliminate the
middlemen’!), to multiple add-on providers, and to multiple OEMs, as well as to their
industry and national cyber-security apparatuses.
Kondratieff Medalist Tessaleno Devezas (2004) called this ‘digital Darwinism,’ and
offered a way to comprehend it: ‘As systems increase in complexity, it becomes necessary
to draw upon social experiences to provide the necessary analogies.’ Researching this
stage of development requires combining qualitative and dialog techniques with such
quantitative methods as may be applicable, and – very cautiously – using the tools of
complexity theory.
I once glibly wrote (Phillips and Kim 1996) that future researchers will use maximum
likelihood estimators and the Lyapunov exponent in tandem. However, they must
proceed cautiously because:
• Chaotic transitions are exquisitely sensitive to initial conditions, which we may
measure more precisely in physics but only loosely in social sciences.
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• Deterministic, chaos-generating processes may exist in the social world, but as
physicist Max Born (1949) remarked, chance (at least in the form of measurement
error) is still king.
• Devezas (2004) added, ‘The more complex and intangible the system, the more
useful is the resort to metaphors.’ In the absence of rigorous correspondence rules
– and they are usually absent – ideas like ‘edge of chaos’ when applied to social
systems are only metaphors (Phillips and Su 2013). As we are taught in school to
avoid reasoning by analogy, social scientists must step lightly when applying the
tools of complexity theory.
Step lightly, but realize that Schumpeter and Kondratieff (See Grinin, Devezas, and
Korotayev 2014) were correct: Socio-economic processes are nonlinear. Massive inter-
connections ensure it. We must learn how to deal with it.
Climate change is perhaps the most important case in point. Climate change, subject to
multiple feedback loops, will proceed at different rates and have differential impacts on
diverse geographies. The physical, biological, and social sciences are challenged to decide
what to measure (Phillips 2014) and, on the social science side, to deal with the resulting
migrations and changes in diets, lifestyles, and health that will eventuate.
Less apocalyptically but also perplexing, the nonlinearity implies an uncertainty principle
of market segmentation: A radical new product cannot be targeted to a customer’s usage
situation, as Clayton Christensen (2003) would have it, because the product changes the
situation (Phillips 2016).
Bridging obsolete and yet-to-come strategic theories
Multiple inter-connections, and especially the digital convergence, have destroyed much
of the received wisdom of strategy, as the interconnections and convergence have
dissolved industry boundaries. After all, we can hardly maintain a sustainable competitive
advantage when we do not know who the competitors are or will be. The ideas of the
philosopher of science Karl Popper suggest a bridging tool that can help guide
organizations until new strategic theories emerge (Phillips, Lin, and Lin 2017).
Another philosopher, Daniel Dennett (2017) distinguished Popper’s approach by defining
four grades of umwelt, or organismic experience. Dennett’s first two grades are instinctive
‘Darwinian creatures,’ capable of no adaptive behavior, and ‘Skinnerian creatures,
who… adjust their behavior in reaction to “reinforcement,”’ with adaptive but random
behaviors being reinforced. Third and fourth in Dennett’s taxonomy are:
3. ‘Popperian creatures, who… pretest hypothetical behaviors offline, letting
"their hypothesis die in their stead," as Karl Popper once put it. Eventually
they must act in the real world, but their first choice is not random, having
won [competitive] trial runs in the internal environment.’
4. ‘Gregorian creatures, named in honor of Richard Gregory, the
psychologist who emphasized the role of “thinking tools”…. The
Gregorian creature’s umwelt is well stocked with thinking tools, both
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abstract and concrete: arithmetic and democracy and double-blind studies,
and microscopes, maps, and computers.’
Theories and firm, well-structured knowledge are Gregorian. Experience-based
hypotheses and exploratory experimentation are Popperian. In the absence of well-
structured strategic knowledge, but with the help of computers, governments and
companies can conduct multiple experiments across multiple geographies and multiple
divisions, sharing information about what works under what conditions, and matching
solutions over there with problems back here. (Popper called this ‘piecemeal
engineering.’) This procedure offers the best chance for organizations to maintain
themselves until new strategic theories emerge and Gregorian thinking again becomes
practicable.
Outliers and risk
I now turn to the word ‘divergence’ in the title of my talk, with special reference to ‘big
data’ and sustainability.
Big data: Attending to outliers
In a world of multiples, analytics researchers should focus on identifying outliers and new
trends, rather than on the averaging and classifying functions well known in classical
statistics. This is one of the unique potentials of big data, relative to small-sample
statistics; more data means more outliers and greater chances that some of them are
meaningful. Big data will finally allow us to comprehend the world’s great diversity,
rather than simply compute regressions to the mean. For many decades, statistical
analysis has been about minimizing sums of squared deviations from a mean—but not
about recognizing the meaning of the deviations. And the ‘standard deviation’ is a clumsy
representation of diversity.
With big data, this can now begin to change. Rather than throwing away ‘troublesome’
data points, we may track them and investigate them. Computers will continue to lack the
contextual knowledge that enables an experienced human to judge whether an anomaly
is a data error or a new and significant phenomenon. Efficiency requires that human-
computer interactive systems be designed to deal with the huge volume of ‘exceptions’
that sleep in big data repositories (Phillips 2017).
Innovation for sustainability
It should be clear that innovation is needed to bring new technologies to bear on the
question of environmental sustainability, and that this innovation is often provided by
new ventures, many of them quite risky from a market perspective. This is not allowed in
the very conservative Brundtland (World Commission 1987) definition of sustainability,
which prohibits bequeathing increased risk – even financial risk – to future generations.
Since I pointed out this idea in a talk to the World Technopolis Association (Phillips
2014a) and in the proceedings of the systems society (Phillips 2013), it has been taken up
by my friend Sten Thore (2016) (teacher of economics Nobel laureate Finn Kydland) and
by a number of other authors now forging links between entrepreneurship and
sustainability. Risk is normally treated as a statistical variance, so the risk and promise of
new ventures also fit my theme of ‘divergence.’
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The knowledge society: It’s about power
In the 1970s, a famous economist lectured about the hypothetical emergence of an
exchange economy between two sailors stranded on a desert island. ‘What,’ I asked,
‘prevents one sailor from hitting the other on the head and simply taking the goods?’ The
lecturer answered, ‘Well, we assume he won’t.’ This reply left me with a certain skepticism
about economic theory. It seemed to me that the central question is power, not price.
As Mr. Piketty (2014) has demonstrated, economic inequality – another kind of
divergence – is growing, and probably menaces the social fabric. What might the rich do
with their money? The disastrous “Citizens United” US Supreme Court decision
provides an avenue: The rich use money to buy elections. Money is a means; power is the
end.
Marx, Schumpeter, and Drucker all believed that knowledge would overtake capital as
the dominant factor of production. It is coming to pass. In several US high tech
companies, rank and file knowledge workers – who are very difficult for the companies to
replace – dictate the firm’s political stance. The employees’ influence in this regard
outweighs that of the capital investors on the company’s board. The companies locate
their headquarters not for the convenience of the CEO, as was the practice in the past,
but where the local quality of life will attract knowledge workers.
Capital and knowledge now share, and compete for, power. The balance is shifting
toward knowledge. In 1993 Drucker lamented that we do not have a theory for a
knowledge economy; it remains true in 2017.
Marx, Schumpeter, and Drucker were well-versed in sociology as well as economics.
Understanding the knowledge revolution will require the multiple perspectives of the
various social sciences.
Challenges and champions
There are other challenges facing social sciences. One of the most important, it seems to
me, is this: Despite many decades of research in organizational behavior and
organizational development, most business firms remain terribly dysfunctional, suffering
from office politics and strategic mis-alignment, and surviving only because their
competitors are even less competent. The same is true of many government agencies,
NGOs, and universities. We are not sure whether this is due to principal-agent problems,
the demise of job security, inability to assess job candidates, shortcomings in management
education (Golden et al 2016), or any of a host of other issues.
We might conjecture that organizational dysfunction is partly explained by the multiplicity
of human motivations, and the divergence between those motivations and the firm’s
strategic goals. However, time has allowed me to focus today only on challenges more
centrally related to multiplicity and divergence – challenges which I believe are
surmountable with sufficient good will and computer advances. Organizational
dysfunction, though extremely troubling, will have to wait for another occasion.
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The list of earlier Kondratieff Medalists shows names – Marchetti, Devezas, Modelski –
that have been closely associated with the journal Technological Forecasting & Social Change. I
mention also Andrey Korotayev and Leonid Grinin, who have lent their enthusiastic
support to the journal. Without doubt, I am wearing the Medal today in large part due to
the high regard the Academy and the Foundation hold for this journal which has been
the outlet for so many Kondratieff studies. 1 The authors and editorial board of
Technological Forecasting & Social Change have helped further internationalize this
international journal and raise it to its current impact factor of 2.625 (5-year factor
=3.226), quite a remarkable number for a journal that is not affiliated with a professional
association.
I would not be here today if not for my very outstanding mentors. My PhD advisor
Abraham Charnes, founding director of the Center for Cybernetic Studies at the
University of Texas, which he named after the laboratory of Yablonsky and Lyapunov
here in Russia. William W. Cooper, who with Charnes was a pioneer in operations
research and a co-winner of the Von Neumann Medal. George Kozmetsky, 2nd-
generation Russian-American entrepreneur, educator, and winner of the US National
Medal of Technology. And Hal Linstone, founding editor of Technological Forecasting &
Social Change. Each generation of scholars works in a different era, using newer tools to
attack newer problems. Though we may wish to, we cannot exactly emulate our teachers.
We can only thank them for their achievements, for their formative influences on us, and
for allowing us to stand on their shoulders. We can only hope they would be proud of us.
I close with an appreciation of my dear family – my parents, my wife, and my two
daughters. Their support, their understanding – and their varied perspectives! – have
helped lead to this day and this celebration.
Thank you.
REFERENCES
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Devezas, T. C. 2004. Paper 2 : Evolutionary Theory of Technological Change : State-of-the-art and New
Approaches. In EU-US Seminar: New Technology Foresight, Forecasting & Assessment Methods (pp. 33–57).
Seville.
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Golden, L., Hsieh, J., Ingene, C., and Phillips, F. Y. 2016. “Business Schools in Crisis.” Journal of Open
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